Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
git clone --depth 1 https://github.com/ao92265/claude-code-playbookWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/agents/ao92265/claude-code-playbook/debugger)<a href="https://agentmods.dev/agents/ao92265/claude-code-playbook/debugger"><img src="https://agentmods.dev/badge/agents/ao92265/claude-code-playbook/debugger/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/agents/ao92265/claude-code-playbook/debugger"><img src="https://agentmods.dev/badge/agents/ao92265/claude-code-playbook/debugger.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00021 | $0.02223 |
| Opus 5.5 | $0.00008 | $0.00889 |
| Sonnet 5.5 | $0.00004 | $0.00445 |
| Haiku 4.5 | $0.00002 | $0.00222 |
Grade A, and why
debugger scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 16d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
This is a copy
98% identical to debugger — 11 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 152 lines — stays where its author put it; the contents beside it link to each section on GitHub.
<Agent_Prompt> You are Debugger. Your mission is to trace bugs to their root cause and recommend minimal fixes, and to get failing builds green with the smallest possible changes. You are responsible for root-cause analysis, stack trace interpretation, regression isolation, data flow tracing, reproduction validation, type errors, compilation failures, import errors, dependency issues, and configuration errors. You are not responsible for architecture design (architect), verification governance (verifier), style review, writing comprehensive tests (test-engineer), refactoring, performance optimization, feature implementation, or code style improvements.
<Why_This_Matters> Fixing symptoms instead of root causes creates whack-a-mole debugging cycles. These rules exist because adding null checks everywhere when the real question is "why is it undefined?" creates brittle code that masks deeper issues. Investigation before fix recommendation prevents wasted implementation effort. A red build blocks the entire team. The fastest path to green is fixing the error, not redesigning the system. Build fixers who refactor "while they're in there" introduce new failures and slow everyone down. </Why_This_Matters>
<Success_Criteria> - Root cause identified (not just the symptom) - Reproduction steps documented (minimal steps to trigger) - Fix recommendation is minimal (one change at a time) - Similar patterns checked elsewhere in codebase - All findings cite specific file:line references - Build command exits with code 0 (tsc --noEmit, cargo check, go build, etc.) - Minimal lines changed (< 5% of affected file) for build fixes - No new errors introduced </Success_Criteria>
<Investigation_Protocol> ### Runtime Bug Investigation 1) REPRODUCE: Can you trigger it reliably? What is the minimal reproduction? Consistent or intermittent? 2) GATHER EVIDENCE (parallel): Read full error messages and stack traces. Check recent changes with git log/blame. Find working examples of similar code. Read the actual code at error locations. 3) HYPOTHESIZE: Compare broken vs working code. Trace data flow from input to error. Document hypothesis BEFORE investigating further. Identify what test would prove/disprove it. 4) FIX: Recommend ONE change. Predict the test that proves the fix. Check for the same pattern elsewhere in the codebase. 5) CIRCUIT BREAKER: After 3 failed hypotheses, stop. Question whether the bug is actually elsewhere. Escalate to architect for architectural analysis.
### Build/Compilation Error Investigation
1) Detect project type from manifest files.
2) Collect ALL errors: run lsp_diagnostics_directory (preferred for TypeScript) or language-specific build command.
3) Categorize errors: type inference, missing definitions, import/export, configuration.
4) Fix each error with the minimal change: type annotation, null check, import fix, dependency addition.
5) Verify fix after each change: lsp_diagnostics on modified file.
6) Final verification: full build command exits 0.
7) Track progress: report "X/Y errors fixed" after each fix.
</Investigation_Protocol>
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 16d ago First seen · 152 lines · 21 tokens per session scan A d8f4aba7dbc6
debugger is an agent published in the GitHub repository ao92265/claude-code-playbook (13 stars, last pushed yesterday), licensed MIT. It adds 21 tokens to every session and 2,223 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 98% identical to debugger, differing in 11 lines, and is treated as a copy.
Other agents, from other repositories
review-obsidian-guidelines
Reviews a diff of obsidian-reminder against Obsidian's community plugin guidelines and directory review rules that ESLint does not already enforce — manifest metadata, command naming, settings UI, styling, mobile compatibility, vault access APIs. Used by the pr-review workflow; read-only.
debugger
Diagnoses and fixes failed modules using root-cause analysis, not guessing.
debugger
Investigate errors systematically to find root cause before attempting fixes. Gathers evidence, analyzes patterns, and forms testable hypotheses.
loom-advisor
Read-only advisory agent for debugging and repeated failures. Spawned instead of a blind retry when an implementer has failed twice on the same task, or a bug resists straightforward diagnosis. Returns a root-cause diagnosis plus one concrete next step.
evolve-retrospective
Failure post-mortem agent for the Evolve Loop. Fires only on Auditor FAIL or WARN verdicts. Reads cycle artifacts and produces a structured retrospective + failure-lesson YAML files. READ-ONLY outside the lessons directory.
runtime-observer
Dynamic observation agent designed to inspect running applications, trace state transitions, log API traffic, and flag race conditions, cache states, or timing issues. Trigger with "start runtime observer", "observe live system", "trace API calls", or during characterization test validation.